Features Extraction and Adaptive Neuro Fuzzy Inference Systems Classification for False Positives Reduction in Mammographic Images

S. Julian Savari Antony; Dr. S. Ravi

Breast cancer is one of the most common neoplasms in women and it is a leading cause of death worldwide. A proper screening procedure can help an early diagnosis of the tumor so reducing the death risk. A suitable computer aided detection system can help the radiologist to detect many subtle signs, normally missed during the screening phase, submitting to the radiologist’s attention those regions that could contain an abnormality. However, one of the most critical problem deals with a suitable tradeoff regarding the number of suspicious zones to present to the radiologist and the capability of identifying the correct ones. In this work, optimal set of features selected by Genetic algorithm are fed as input to Adaptive Neuro fuzzy inference system for classification of images into normal, suspect and abnormal categories. The classification of suspicious signs into normal tissue or massive lesions has been faced in order to get a False Positive Reduction without noticeably affecting the number of True Positives

Select Volume / Issues:

• Volume 6 Issue 2, Mar. 2015

Year:

2015

Type of Publication:

Article

Keywords:

Breast Cancer; Mammographic Technique; ANFIS; Region of Interest; False Positives

Journal:

IJECCE

Volume:

6

Number:

2

Pages:

261-264

Month:

March

ISSN:

2249-071X